在肺炎呼吸信号中的喘和裂歧视算法
Jaewon Seong1, Bengie L Ortiz2, Jo Woon Chong3
1Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX.
概括
一种新型肺炎检测方法使用随机森林算法的两步层次方法来准确地从呼吸声中识别肺炎,并区分喘息和声. 这种方法在肺炎检测和声音区分方面都具有很高的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 呼吸系统医学 呼吸系统医学
- 机器学习 机器学习
背景情况:
- 从呼吸道声音检测肺炎对于及时诊断和治疗至关重要.
- 在肺炎患者中区分口哨和声有助于向治疗.
- 现有的方法可能缺乏准确性或区分特定呼吸道声音的能力.
研究的目的:
- 提出一种新的,两步层次的方法,用于在呼吸道声信号中检测肺炎.
- 为了提高肺炎检测性能,并增加喘息/裂歧视能力.
- 为了促进基于特定呼吸声特征的适当补救措施的应用.
主要方法:
- 开发了一种两步的层次分类方法.
- 采用重新抽样技术来解决ICBHI肺炎数据集中的数据不平衡.
- 随机森林算法被用于肺炎分类和喘息/裂歧视.
主要成果:
- 拟议的方法在通过呼吸声检测肺炎时达到85.40%的准确性.
- 该系统在肺炎病例中显示出82.70%的精度来区分口哨和声.
- 基于森林的随机分层方法显示,ICBHI呼吸数据集的性能有所改善.
结论:
- 开发的层次性随机森林方法为肺炎检测和呼吸声特性提供了有效的方法.
- 这种方法可以提高诊断准确度,并指导肺炎患者的临床决策.
- 声区分的整合提高了自动呼吸声分析的临床适用性.
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